hpgltools Differential Expression Analyses Using the Fission Dataset

1 Example hpgltool usage with a real data set (fission)

This document aims to provide further examples in how to use the hpgltools.

Note to self, the header has rmarkdown::pdf_document instead of html_document or html_vignette because it gets some bullcrap error ‘margins too large’…

1.1 Setting up

Here are the commands I invoke to get ready to play with new data, including everything required to install hpgltools, the software it uses, and the fission data.

## These first 4 lines are not needed once hpgltools is installed.
## source("http://bioconductor.org/biocLite.R")
## biocLite("devtools")
## library(devtools)
## install_github("elsayed-lab/hpgltools")
library(hpgltools)
require.auto("fission")

## [1] 0

tt <- sm(library(fission))
tt <- data(fission)

1.2 Data import

All the work I do in Dr. El-Sayed’s lab makes some pretty hard assumptions about how data is stored. As a result, to use the fission data set I will do a little bit of shenanigans to match it to the expected format. Now that I have played a little with fission, I think its format is quite nice and am likely to have my experiment class instead be a SummarizedExperiment.

## Extract the meta data from the fission dataset
meta <- as.data.frame(fission@colData)
## Make conditions and batches
meta$condition <- paste(meta$strain, meta$minute, sep=".")
meta$batch <- meta$replicate
meta$sample.id <- rownames(meta)
## Grab the count data
fission_data <- fission@assays$data$counts
## This will make an experiment superclass called 'expt' and it contains
## an ExpressionSet along with any arbitrary additional information one might want to include.
## Along the way it writes a Rdata file which is by default called 'expt.Rdata'
fission_expt <- create_expt(metadata=meta, count_dataframe=fission_data)

## Bringing together the count matrix and gene information.

2 Some simple differential expression analyses

Travis wisely imposes a limit on the amount of time for building vignettes. My tools by default will attempt all possible pairwise comparisons, which takes a long time. Therefore I am going to take a subset of the data and limit these comparisons to that.

fun_data <- expt_subset(fission_expt, subset="condition=='wt.120'|condition=='wt.30'")
fun_norm <- sm(normalize_expt(fun_data, batch="limma", norm="quant", transform="log2", convert="cpm"))

2.1 Try using limma first

limma_comparison <- sm(limma_pairwise(fun_data))

names(limma_comparison$all_tables)

## [1] "wt.30_vs_wt.120"

summary(limma_comparison$all_tables$wt.30_vs_wt.120)

##      logFC           AveExpr            t             P.Value      
##  Min.   :-4.566   Min.   :-4.58   Min.   :-28.97   Min.   :0.0000  
##  1st Qu.:-0.660   1st Qu.: 1.11   1st Qu.: -3.87   1st Qu.:0.0077  
##  Median :-0.304   Median : 3.97   Median : -1.49   Median :0.0661  
##  Mean   :-0.263   Mean   : 3.11   Mean   : -1.61   Mean   :0.2171  
##  3rd Qu.: 0.038   3rd Qu.: 5.44   3rd Qu.:  0.20   3rd Qu.:0.3718  
##  Max.   : 6.849   Max.   :18.59   Max.   : 36.32   Max.   :0.9992  
##    adj.P.Val            B             qvalue       
##  Min.   :0.0030   Min.   :-7.71   Min.   :0.00077  
##  1st Qu.:0.0309   1st Qu.:-5.84   1st Qu.:0.00793  
##  Median :0.1321   Median :-4.72   Median :0.03387  
##  Mean   :0.2746   Mean   :-4.03   Mean   :0.07039  
##  3rd Qu.:0.4957   3rd Qu.:-2.49   3rd Qu.:0.12710  
##  Max.   :0.9992   Max.   : 6.03   Max.   :0.25610

scatter_wt_mut <- extract_coefficient_scatter(limma_comparison, type="limma", x="wt.30", y="wt.120", gvis_filename=NULL)

## This can do comparisons among the following columns in the pairwise result:

## wt.120, wt.30, wt.30_vs_wt.120

## Actually comparing wt.30 and wt.120.

scatter_wt_mut$scatter

scatter_wt_mut$both_histogram$plot + ggplot2::scale_y_continuous(limits=c(0,0.20))

## Warning: Removed 5 rows containing missing values (geom_bar).

ma_wt_mut <- extract_de_ma(limma_comparison, type="limma")
ma_wt_mut$plot

2.2 Then DESeq2

deseq_comparison <- sm(deseq2_pairwise(fun_data))
summary(deseq_comparison$all_tables$wt.30_vs_wt.120)

##     baseMean           logFC          lfcSE          stat      
##  Min.   :      0   Min.   :-3.9   Min.   :0.1   Min.   :-20.9  
##  1st Qu.:     28   1st Qu.:-0.3   1st Qu.:0.2   1st Qu.: -1.2  
##  Median :    192   Median : 0.0   Median :0.2   Median :  0.0  
##  Mean   :   1703   Mean   : 0.0   Mean   :0.3   Mean   :  0.2  
##  3rd Qu.:    536   3rd Qu.: 0.3   3rd Qu.:0.3   3rd Qu.:  1.2  
##  Max.   :4924000   Max.   : 6.4   Max.   :0.5   Max.   : 30.4  
##                    NA's   :404    NA's   :404   NA's   :404    
##     P.Value         adj.P.Val          qvalue      
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.0206   1st Qu.:0.0717   1st Qu.:0.0822  
##  Median :0.2567   Median :0.4697   Median :0.5133  
##  Mean   :0.3604   Mean   :0.4797   Mean   :0.4972  
##  3rd Qu.:0.6558   3rd Qu.:0.8612   3rd Qu.:0.8742  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
## 

scatter_wt_mut <- extract_coefficient_scatter(deseq_comparison, type="deseq", x="wt.30", y="wt.120", gvis_filename=NULL)

## This can do comparisons among the following columns in the pairwise result:

## wt.120, wt.30

## Actually comparing wt.30 and wt.120.

scatter_wt_mut$scatter

ma_wt_mut <- extract_de_ma(deseq_comparison, type="deseq")
ma_wt_mut$plot

2.3 And EdgeR

edger_comparison <- sm(edger_pairwise(fun_data, model_batch=TRUE))
scatter_wt_mut <- extract_coefficient_scatter(edger_comparison, type="edger", x="wt.30", y="wt.120", gvis_filename=NULL)

## This can do comparisons among the following columns in the pairwise result:

## wt.120, wt.30

## Actually comparing wt.30 and wt.120.

scatter_wt_mut$scatter

ma_wt_mut <- extract_de_ma(edger_comparison, type="edger")
ma_wt_mut$plot

2.4 My stupid basic comparison

basic_comparison <- sm(basic_pairwise(fun_data))
summary(basic_comparison$all_tables$wt.30_vs_wt.120)

##  numerator_median denominator_median numerator_var      denominator_var   
##  Min.   :-2.73    Min.   :-3.60      Length:5505        Length:5505       
##  1st Qu.: 3.31    1st Qu.: 3.31      Class :character   Class :character  
##  Median : 4.65    Median : 4.63      Mode  :character   Mode  :character  
##  Mean   : 4.71    Mean   : 4.71                                           
##  3rd Qu.: 5.94    3rd Qu.: 5.93                                           
##  Max.   :18.61    Max.   :18.61                                           
##        t               p                 logFC            adjp          
##  Min.   :-49.10   Length:5505        Min.   :-4.263   Length:5505       
##  1st Qu.: -1.53   Class :character   1st Qu.:-0.406   Class :character  
##  Median :  0.39   Mode  :character   Median :-0.070   Mode  :character  
##  Mean   :  0.16                      Mean   : 0.008                     
##  3rd Qu.:  2.10                      3rd Qu.: 0.297                     
##  Max.   : 50.21                      Max.   : 7.485

scatter_wt_mut <- extract_coefficient_scatter(basic_comparison, type="basic")

## This can do comparisons among the following columns in the pairwise result:

## wt.120, wt.30

## Actually comparing wt.120 and wt.30.

scatter_wt_mut$scatter

ma_wt_mut <- extract_de_ma(basic_comparison, type="basic")
ma_wt_mut$plot

2.5 Combine them all

all_comparisons <- sm(all_pairwise(fun_data, model_batch=TRUE))
all_combined <- sm(combine_de_tables(all_comparisons, excel=FALSE))
head(all_combined$data[[1]])

##                      name limma_logfc limma_adjp deseq_logfc deseq_adjp
## SPAC1002.01   SPAC1002.01    -1.31300    0.07658    -0.62290    0.42830
## SPAC1002.02   SPAC1002.02    -0.31570    0.40850     0.01307    0.98340
## SPAC1002.03c SPAC1002.03c    -0.53340    0.01203    -0.22400    0.23820
## SPAC1002.04c SPAC1002.04c     0.03014    0.91240     0.31720    0.33580
## SPAC1002.05c SPAC1002.05c     0.47500    0.07282     0.71490    0.01158
## SPAC1002.06c SPAC1002.06c     0.42120    0.70020     0.42400    0.62730
##              edger_logfc edger_adjp limma_ave limma_t limma_b limma_p
## SPAC1002.01     -1.05900  0.2201000   -0.2209 -3.1750 -3.1790 0.03103
## SPAC1002.02     -0.02342  1.0000000    2.8450 -1.2080 -6.1280 0.29020
## SPAC1002.03c    -0.23630  0.1598000    7.0880 -7.3990 -0.8416 0.00137
## SPAC1002.04c     0.32580  0.2151000    4.2010  0.1543 -7.3490 0.88440
## SPAC1002.05c     0.74120  0.0009489    3.9140  3.2460 -4.0540 0.02889
## SPAC1002.06c     0.69240  0.7328000   -1.8560  0.5183 -5.8280 0.63010
##               limma_q deseq_basemean deseq_lfcse deseq_stat  deseq_p
## SPAC1002.01  0.019630         11.150      0.5225   -1.19200 0.233200
## SPAC1002.02  0.104700         87.420      0.3035    0.04307 0.965600
## SPAC1002.03c 0.003084       1621.000      0.1365   -1.64100 0.100700
## SPAC1002.04c 0.233900        222.200      0.2273    1.39600 0.162800
## SPAC1002.05c 0.018660        187.200      0.2342    3.05300 0.002268
## SPAC1002.06c 0.179500          4.176      0.5310    0.79840 0.424600
##              deseq_q edger_logcpm  edger_lr  edger_p    edger_q
## SPAC1002.01  0.48450      0.06691  2.745000 0.097570  2.201e-01
## SPAC1002.02  1.00000      2.89400  0.007429 0.931300  1.000e+00
## SPAC1002.03c 0.27230      7.09500  3.399000 0.065220  1.598e-01
## SPAC1002.04c 0.38150      4.24700  2.794000 0.094620  2.151e-01
## SPAC1002.05c 0.01331      3.99900 14.270000 0.000158  9.489e-04
## SPAC1002.06c 0.69730     -0.89280  0.370800 0.542600  7.328e-01
##              basic_nummed basic_denmed basic_numvar basic_denvar
## SPAC1002.01         0.000        0.000            0            0
## SPAC1002.02         3.100        2.774    3.603e-01    2.890e-02
## SPAC1002.03c        6.909        7.248    2.955e-03    1.016e-03
## SPAC1002.04c        4.407        4.195    5.418e-02    1.441e-01
## SPAC1002.05c        4.272        3.625    1.293e-01    5.645e-02
## SPAC1002.06c        0.000        0.000            0            0
##              basic_logfc  basic_t   basic_p basic_adjp fc_meta    fc_var
## SPAC1002.01       0.0000  0.00000         0          0 -0.9855 1.289e-02
## SPAC1002.02       0.3260 -0.01124 9.919e-01  9.963e-01 -0.1083 2.378e-04
## SPAC1002.03c     -0.3390  9.25600 1.969e-03  3.718e-02 -0.3344 9.292e-04
## SPAC1002.04c      0.2125 -1.26900 2.862e-01  4.542e-01  0.2261 7.575e-04
## SPAC1002.05c      0.6472 -2.95900 4.975e-02  1.630e-01  0.6594 8.969e-03
## SPAC1002.06c      0.0000  0.00000         0          0  0.5031 9.696e-03
##              fc_varbymed    p_meta     p_var
## SPAC1002.01   -1.308e-02 1.206e-01 1.062e-02
## SPAC1002.02   -2.195e-03 7.290e-01 1.447e-01
## SPAC1002.03c  -2.779e-03 5.576e-02 2.534e-03
## SPAC1002.04c   3.351e-03 3.806e-01 1.915e-01
## SPAC1002.05c   1.360e-02 1.044e-02 2.565e-04
## SPAC1002.06c   1.927e-02 5.324e-01 1.064e-02

sig_genes <- sm(extract_significant_genes(all_combined, excel=FALSE))
head(sig_genes$limma$ups[[1]])

##                        name limma_logfc limma_adjp deseq_logfc deseq_adjp
## SPBC2F12.09c   SPBC2F12.09c       6.849   0.003383       6.399  3.871e-85
## SPAC22A12.17c SPAC22A12.17c       5.425   0.003940       4.209  1.520e-17
## SPAPB1A11.02   SPAPB1A11.02       5.384   0.004294       3.479  4.042e-10
## SPCPB16A4.07   SPCPB16A4.07       5.384   0.003001       5.518 1.569e-199
## SPNCRNA.1611   SPNCRNA.1611       5.038   0.003415       4.328  2.681e-22
## SPBC660.05       SPBC660.05       5.006   0.005580       3.808  1.092e-13
##               edger_logfc edger_adjp limma_ave limma_t limma_b   limma_p
## SPBC2F12.09c        7.170 1.264e-180     2.605   19.77  2.1880 2.432e-05
## SPAC22A12.17c       5.822  3.155e-57     6.521   15.12  2.6270 7.489e-05
## SPAPB1A11.02        6.483  1.257e-14    -1.189   13.46  0.5824 8.846e-05
## SPCPB16A4.07        5.684 4.519e-138     6.573   28.53  5.0270 5.176e-06
## SPNCRNA.1611        5.529  1.789e-33     0.578   16.96  1.7660 3.215e-05
## SPBC660.05          5.310  9.868e-74     3.655   12.22  1.6730 1.819e-04
##                 limma_q deseq_basemean deseq_lfcse deseq_stat    deseq_p
## SPBC2F12.09c  0.0008669         443.50      0.3220     19.870  6.955e-88
## SPAC22A12.17c 0.0010100        4289.00      0.4700      8.956  3.378e-19
## SPAPB1A11.02  0.0011010          21.20      0.5178      6.720  1.816e-11
## SPCPB16A4.07  0.0007693        4157.00      0.1813     30.430 2.563e-203
## SPNCRNA.1611  0.0008754          58.57      0.4276     10.120  4.424e-24
## SPBC660.05    0.0014300         523.80      0.4837      7.872  3.478e-15
##                  deseq_q edger_logcpm edger_lr    edger_p    edger_q
## SPBC2F12.09c   4.451e-85       5.2210   839.00 1.796e-184 1.264e-180
## SPAC22A12.17c  1.748e-17       8.4930   264.90  1.479e-59  3.155e-57
## SPAPB1A11.02   4.648e-10       0.9166    65.83  4.910e-16  1.257e-14
## SPCPB16A4.07  1.804e-199       8.4490   641.90 1.284e-141 4.519e-138
## SPNCRNA.1611   3.083e-22       2.3170   154.00  2.363e-35  1.789e-33
## SPBC660.05     1.255e-13       5.4560   341.60  2.804e-76  9.869e-74
##               basic_nummed basic_denmed basic_numvar basic_denvar
## SPBC2F12.09c         6.250       -1.235    2.211e-02    5.043e-01
## SPAC22A12.17c        9.396        4.087    2.236e-02    9.694e-01
## SPAPB1A11.02         1.491       -3.604    6.869e-01    4.981e-01
## SPCPB16A4.07         9.416        3.641    2.022e-02    2.894e-01
## SPNCRNA.1611         3.380       -1.604    1.174e-01    1.141e-01
## SPBC660.05           6.156        1.381    2.068e-01    5.143e-01
##               basic_logfc basic_t   basic_p basic_adjp fc_meta    fc_var
## SPBC2F12.09c        7.485 -16.760 2.432e-03  3.928e-02   6.806 0.000e+00
## SPAC22A12.17c       5.309 -10.080 8.325e-03  6.437e-02   5.279 2.126e-01
## SPAPB1A11.02        5.095  -7.708 1.687e-03  3.452e-02   4.994 1.105e+00
## SPCPB16A4.07        5.776 -17.480 1.780e-03  3.524e-02   5.381 1.775e-01
## SPNCRNA.1611        4.984 -18.270 5.286e-05  1.455e-02   5.001 1.141e-02
## SPBC660.05          4.775 -10.840 9.581e-04  2.791e-02   4.618 1.699e-01
##               fc_varbymed    p_meta     p_var
## SPBC2F12.09c    0.000e+00 8.107e-06 1.972e-10
## SPAC22A12.17c   4.026e-02 2.496e-05 1.870e-09
## SPAPB1A11.02    2.213e-01 2.949e-05 2.608e-09
## SPCPB16A4.07    3.299e-02 1.725e-06 8.930e-12
## SPNCRNA.1611    2.281e-03 1.072e-05 3.445e-10
## SPBC660.05      3.680e-02 6.063e-05 1.103e-08

## Here we see that edger and deseq agree the least:
all_comparisons$comparison$comp

##    wt.30_vs_wt.120
## le          0.9666
## ld          0.9371
## ed          0.8442
## lb          0.9759
## eb          0.9782
## db          0.9715

## And here we can look at the set of 'significant' genes according to various tools:
yeast_sig <- extract_significant_genes(all_combined, excel=FALSE)

## Writing excel data sheet 0/1: wt.30_vs_wt.120

## Assuming the fold changes are on the log scale and so taking >< 0

## After (adj)p filter, the up genes table has 529 genes.

## After (adj)p filter, the down genes table has 1786 genes.

## Assuming the fold changes are on the log scale and so taking -1.0 * fc

## After fold change filter, the up genes table has 280 genes.

## After fold change filter, the down genes table has 563 genes.

## Still not printing excel sheets for the significant genes.

## Writing excel data sheet 1/1: wt.30_vs_wt.120

## Assuming the fold changes are on the log scale and so taking >< 0

## After (adj)p filter, the up genes table has 1107 genes.

## After (adj)p filter, the down genes table has 1052 genes.

## Assuming the fold changes are on the log scale and so taking -1.0 * fc

## After fold change filter, the up genes table has 447 genes.

## After fold change filter, the down genes table has 278 genes.

## Still not printing excel sheets for the significant genes.

## Writing excel data sheet 2/1: wt.30_vs_wt.120

## Assuming the fold changes are on the log scale and so taking >< 0

## After (adj)p filter, the up genes table has 899 genes.

## After (adj)p filter, the down genes table has 716 genes.

## Assuming the fold changes are on the log scale and so taking -1.0 * fc

## After fold change filter, the up genes table has 382 genes.

## After fold change filter, the down genes table has 186 genes.

## Still not printing excel sheets for the significant genes.

## Writing excel data sheet 3/1: wt.30_vs_wt.120

## Assuming the fold changes are on the log scale and so taking >< 0

## After (adj)p filter, the up genes table has 277 genes.

## After (adj)p filter, the down genes table has 237 genes.

## Assuming the fold changes are on the log scale and so taking -1.0 * fc

## After fold change filter, the up genes table has 185 genes.

## After fold change filter, the down genes table has 105 genes.

## Still not printing excel sheets for the significant genes.

yeast_barplots <- sm(significant_barplots(combined=all_combined))
yeast_barplots$limma

yeast_barplots$edger

yeast_barplots$deseq

2.5.1 Setting up

Since I didn’t acquire this data in a ‘normal’ way, I am going to post-generate a gff file which may be used by clusterprofiler, topgo, and gostats.

Therefore, I am going to make use of TxDb to make the requisite gff file.

limma_results <- limma_comparison$all_tables
## The set of comparisons performed
names(limma_results)

## [1] "wt.30_vs_wt.120"

table <- limma_results$wt.30_vs_wt.120
dim(table)

## [1] 7039    7

gene_names <- rownames(table)

updown_genes <- get_sig_genes(table, p=0.05, fc=0.4, p_column="P.Value")

## Assuming the fold changes are on the log scale and so taking >< 0

## After (adj)p filter, the up genes table has 738 genes.

## After (adj)p filter, the down genes table has 2532 genes.

## Assuming the fold changes are on the log scale and so taking -1.0 * fc

## After fold change filter, the up genes table has 629 genes.

## After fold change filter, the down genes table has 2130 genes.

tt <- require.auto("GenomicFeatures")
tt <- require.auto("biomaRt")
ensembl_pombe <- biomaRt::useMart("fungal_mart", dataset="spombe_eg_gene", host="fungi.ensembl.org")
pombe_filters <- biomaRt::listFilters(ensembl_pombe)
head(pombe_filters, n=20) ## 11 looks to be my guy

##                  name                             description
## 1     chromosome_name                         Chromosome name
## 2               start                                   Start
## 3                 end                                     End
## 4              strand                                  Strand
## 5  chromosomal_region e.g. 1:100:10000:-1, 1:100000:2000000:1
## 6         with_chembl                       with ChEMBL ID(s)
## 7           with_embl                  with ENA/GenBank ID(s)
## 8     with_protein_id          with ENA/GenBank protein ID(s)
## 9     with_entrezgene                   with EntrezGene ID(s)
## 10     with_ec_number       with Enzyme Commission (EC) ID(s)
## 11          with_fypo             with FYPO term accession(s)
## 12   with_ontology_go                           with GO ID(s)
## 13            with_go               with GO term accession(s)
## 14 with_ox_goslim_goa                   with GOSlim GOA ID(s)
## 15          with_kegg                         with KEGG ID(s)
## 16   with_kegg_enzyme                  with KEGG enzyme ID(s)
## 17        with_merops                       with MEROPS ID(s)
## 18       with_metacyc                      with Metacyc ID(s)
## 19           with_mod              with MOD term accession(s)
## 20           with_pdb                          with PDB ID(s)

possible_pombe_attributes <- biomaRt::listAttributes(ensembl_pombe)
##pombe_goids <- biomaRt::getBM(attributes=c('pombase_gene_name', 'go_accession'), filters="biotype",
##                              values=gene_names, mart=ensembl_pombe)
pombe_goids <- biomaRt::getBM(attributes=c('pombase_transcript', 'go_accession'),
                              values=gene_names, mart=ensembl_pombe)
colnames(pombe_goids) <- c("ID","GO")

## This used to work, but does so no longer and I do not know why.
pombe <- sm(GenomicFeatures::makeTxDbFromBiomart(biomart="fungal_mart",
                                                 dataset="spombe_eg_gene",
                                                 host="fungi.ensembl.org"))

## Error in .Ensembl_getMySQLCoreDir(dataset): found 0 or more than 1 subdir for "spombe_eg_gene" dataset at ftp://ftp.ensembl.org/pub/current_mysql/

## This was found at the bottom of: https://www.biostars.org/p/232005/
link <- "ftp://ftp.ensemblgenomes.org/pub/release-34/fungi/gff3/schizosaccharomyces_pombe/Schizosaccharomyces_pombe.ASM294v2.34.gff3.gz"
pombe <- makeTxDbFromGFF(link, format="gff3", organism="Schizosaccharomyces pombe",
                           taxonomyId="4896")

## Error in eval(expr, envir, enclos): could not find function "makeTxDbFromGFF"

pombe_transcripts <- as.data.frame(GenomicFeatures::transcriptsBy(pombe))

## Error in GenomicFeatures::transcriptsBy(pombe): object 'pombe' not found

lengths <- pombe_transcripts[, c("group_name","width")]

## Error in eval(expr, envir, enclos): object 'pombe_transcripts' not found

colnames(lengths) <- c("ID","width")

## Error in `colnames<-`(`*tmp*`, value = c("ID", "width")): attempt to set 'colnames' on an object with less than two dimensions

## Something useful I didn't notice before:
## makeTranscriptDbFromGFF()  ## From GenomicFeatures, much like my own gff2df()
gff_from_txdb <- GenomicFeatures::asGFF(pombe)

## Error in GenomicFeatures::asGFF(pombe): object 'pombe' not found

## why is GeneID: getting prefixed to the IDs!?
gff_from_txdb$ID <- gsub(x=gff_from_txdb$ID, pattern="GeneID:", replacement="")

## Error in gsub(x = gff_from_txdb$ID, pattern = "GeneID:", replacement = ""): object 'gff_from_txdb' not found

written_gff <- rtracklayer::export.gff3(gff_from_txdb, con="pombe.gff")

## Error in rtracklayer::export.gff3(gff_from_txdb, con = "pombe.gff"): object 'gff_from_txdb' not found

2.6 GOSeq test

summary(updown_genes)

##            Length Class      Mode
## up_genes   7      data.frame list
## down_genes 7      data.frame list

test_genes <- updown_genes$down_genes
rownames(test_genes) <- paste0(rownames(test_genes), ".1")
lengths$ID <- paste0(lengths$ID, ".1")

## Error in lengths$ID: object of type 'closure' is not subsettable

funkytown <- sm(simple_goseq(sig_genes=test_genes, go_db=pombe_goids, length_db=lengths))

## Error in simple_goseq(sig_genes = test_genes, go_db = pombe_goids, length_db = lengths): This requires either the name of a goseq supported species or an orgdb instance.

head(funkytown$alldata)

## Error in head(funkytown$alldata): object 'funkytown' not found

funkytown$pvalue_plots$mfp_plot

## Error in eval(expr, envir, enclos): object 'funkytown' not found

test_genes <- updown_genes$up_genes
rownames(test_genes) <- paste0(rownames(test_genes), ".1")
funkytown <- sm(simple_goseq(sig_genes=test_genes, go_db=pombe_goids, length_db=lengths))

## Error in simple_goseq(sig_genes = test_genes, go_db = pombe_goids, length_db = lengths): This requires either the name of a goseq supported species or an orgdb instance.

head(funkytown$alldata)

## Error in head(funkytown$alldata): object 'funkytown' not found

funkytown$pvalue_plots$bpp_plot

## Error in eval(expr, envir, enclos): object 'funkytown' not found

index.html

pander::pander(sessionInfo())

R version 3.3.3 (2017-03-06)

**Platform:** x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C

attached base packages: parallel, stats4, methods, stats, graphics, grDevices, utils, datasets and base

other attached packages: ruv(v.0.9.6), fission(v.0.108.0), SummarizedExperiment(v.1.4.0), Biobase(v.2.34.0), GenomicRanges(v.1.26.2), GenomeInfoDb(v.1.10.3), IRanges(v.2.8.1), S4Vectors(v.0.12.1), BiocGenerics(v.0.20.0) and hpgltools(v.2017.01)

loaded via a namespace (and not attached): minqa(v.1.2.4), colorspace(v.1.3-2), colorRamps(v.2.3), rprojroot(v.1.2), qvalue(v.2.6.0), htmlTable(v.1.9), corpcor(v.1.6.8), XVector(v.0.14.1), base64enc(v.0.1-3), ggrepel(v.0.6.5), AnnotationDbi(v.1.36.2), codetools(v.0.2-15), splines(v.3.3.3), doParallel(v.1.0.10), DESeq(v.1.26.0), robustbase(v.0.92-7), geneplotter(v.1.52.0), knitr(v.1.15.1), ade4(v.1.7-6), Formula(v.1.2-1), nloptr(v.1.0.4), Rsamtools(v.1.26.1), pbkrtest(v.0.4-7), annotate(v.1.52.1), cluster(v.2.0.6), compiler(v.3.3.3), backports(v.1.0.5), assertthat(v.0.1), Matrix(v.1.2-8), lazyeval(v.0.2.0), limma(v.3.30.13), acepack(v.1.4.1), htmltools(v.0.3.5), tools(v.3.3.3), gtable(v.0.2.0), reshape2(v.1.4.2), Rcpp(v.0.12.10), Biostrings(v.2.42.1), gdata(v.2.17.0), preprocessCore(v.1.36.0), nlme(v.3.1-131), rtracklayer(v.1.34.2), iterators(v.1.0.8), stringr(v.1.2.0), openxlsx(v.4.0.17), testthat(v.1.0.2), lme4(v.1.1-12), gtools(v.3.5.0), devtools(v.1.12.0), statmod(v.1.4.29), XML(v.3.98-1.5), edgeR(v.3.16.5), DEoptimR(v.1.0-8), zlibbioc(v.1.20.0), MASS(v.7.3-45), scales(v.0.4.1), RColorBrewer(v.1.1-2), yaml(v.2.1.14), memoise(v.1.0.0), gridExtra(v.2.2.1), ggplot2(v.2.2.1), pander(v.0.6.0), biomaRt(v.2.30.0), rpart(v.4.1-10), latticeExtra(v.0.6-28), stringi(v.1.1.3), RSQLite(v.1.1-2), highr(v.0.6), genefilter(v.1.56.0), foreach(v.1.4.3), RMySQL(v.0.10.10), checkmate(v.1.8.2), GenomicFeatures(v.1.26.3), caTools(v.1.17.1), BiocParallel(v.1.8.1), matrixStats(v.0.51.0), bitops(v.1.0-6), evaluate(v.0.10), lattice(v.0.20-34), GenomicAlignments(v.1.10.1), htmlwidgets(v.0.8), labeling(v.0.3), plyr(v.1.8.4), magrittr(v.1.5), variancePartition(v.1.4.2), DESeq2(v.1.14.1), R6(v.2.2.0), gplots(v.3.0.1), Hmisc(v.4.0-2), DBI(v.0.6), foreign(v.0.8-67), withr(v.1.0.2), mgcv(v.1.8-17), survival(v.2.41-2), RCurl(v.1.95-4.8), nnet(v.7.3-12), tibble(v.1.2), crayon(v.1.3.2), KernSmooth(v.2.23-15), rmarkdown(v.1.4), locfit(v.1.5-9.1), grid(v.3.3.3), sva(v.3.22.0), data.table(v.1.10.4), digest(v.0.6.12), xtable(v.1.8-2), genoPlotR(v.0.8.4) and munsell(v.0.4.3)